{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
    "<div class=\"align-center\">\n",
    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
    "</div>\n",
    "\n",
    "To install Unsloth your local device, follow [our guide](https://docs.unsloth.ai/get-started/install-and-update). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
    "\n",
    "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### News"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Introducing FP8 precision training for faster RL inference. [Read Blog](https://docs.unsloth.ai/new/fp8-reinforcement-learning).\n",
    "\n",
    "Unsloth's [Docker image](https://hub.docker.com/r/unsloth/unsloth) is here! Start training with no setup & environment issues. [Read our Guide](https://docs.unsloth.ai/new/how-to-train-llms-with-unsloth-and-docker).\n",
    "\n",
    "[gpt-oss RL](https://docs.unsloth.ai/new/gpt-oss-reinforcement-learning) is now supported with the fastest inference & lowest VRAM. Try our [new notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) which creates kernels!\n",
    "\n",
    "Introducing [Vision](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) and [Standby](https://docs.unsloth.ai/basics/memory-efficient-rl) for RL! Train Qwen, Gemma etc. VLMs with GSPO - even faster with less VRAM.\n",
    "\n",
    "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n    !pip install unsloth\nelse:\n    # Do this only in Colab notebooks! Otherwise use pip install unsloth\n    import torch; v = re.match(r\"[0-9]{1,}\\.[0-9]{1,}\", str(torch.__version__)).group(0)\n    xformers = \"xformers==\" + (\"0.0.33.post1\" if v==\"2.9\" else \"0.0.32.post2\" if v==\"2.8\" else \"0.0.29.post3\")\n    !pip install --no-deps bitsandbytes accelerate {xformers} peft trl triton cut_cross_entropy unsloth_zoo\n    !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n    !pip install --no-deps unsloth\n!pip install transformers==4.56.2\n!pip install --no-deps trl==0.22.2"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsloth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ud83e\udda5 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "\ud83e\udda5 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2025.3.15: Fast Qwen2 patching. Transformers: 4.48.3.\n",
      "   \\\\   /|    NVIDIA A100-SXM4-40GB. Num GPUs = 1. Max memory: 39.557 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False]\n",
      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
     ]
    },
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/5.55G [00:00<?, ?B/s]"
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       "tokenizer_config.json: 0.00B [00:00, ?B/s]"
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       "vocab.json: 0.00B [00:00, ?B/s]"
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    {
     "data": {
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    },
    {
     "data": {
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       "tokenizer.json: 0.00B [00:00, ?B/s]"
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     "metadata": {},
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    }
   ],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "max_seq_length = 32768 # Choose any! We auto support RoPE Scaling internally!\n",
    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
    "\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/Qwen2.5-Coder-0.5B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen2.5-Coder-0.5B-Instruct\",\n",
    "    \"unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen2.5-Coder-1.5B-Instruct\",\n",
    "    \"unsloth/Qwen2.5-Coder-3B-Instruct\",\n",
    "    \"unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen2.5-Coder-14B-Instruct\",\n",
    "    \"unsloth/Qwen2.5-Coder-14B-Instruct-bnb-4bit\",\n",
    "    \"unsloth/Qwen2.5-Coder-32B-Instruct-bnb-4bit\",\n",
    "\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsloth 2025.3.15 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
     ]
    }
   ],
   "source": [
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
    "    lora_alpha = 16,\n",
    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
    "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"Data\"></a>\n",
    "### Dataset\n",
    "\n",
    "We are using the [`open-r1/codeforces-cots`](https://huggingface.co/datasets/open-r1/codeforces-cots/viewer/solutions_decontaminated?views%5B%5D=solutions_decontaminated) by the Hugging Face Open R1 project. This is a dataset of problems for a competitive coding challenge.\n",
    "\n",
    "The Open R1 team have found that models trained to reason on competive coding tasks like this outperform other models on coding benchmarks.\n",
    "\n",
    "\n",
    "![image](https://huggingface.co/open-r1/OlympicCoder-7B/resolve/main/lcb-evals.png)"
   ]
  },
  {
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      "bd28187cbc8a42688625900efa5f6729"
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    },
    "id": "LjY75GoYUCB8",
    "outputId": "b7ababe1-80b3-4e15-99f0-6720d7dde3fd"
   },
   "outputs": [
    {
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       "version_major": 2,
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       "README.md: 0.00B [00:00, ?B/s]"
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     "output_type": "display_data"
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    {
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      "text/plain": [
       "train-00000-of-00014.parquet:   0%|          | 0.00/171M [00:00<?, ?B/s]"
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     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
       "train-00001-of-00014.parquet:   0%|          | 0.00/140M [00:00<?, ?B/s]"
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     "output_type": "display_data"
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    {
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      "text/plain": [
       "train-00002-of-00014.parquet:   0%|          | 0.00/135M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
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      "text/plain": [
       "train-00003-of-00014.parquet:   0%|          | 0.00/128M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {},
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    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41ab0e68f02b4afc8e7877bae7ae0e13",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3632f1ab7caf41cd914754ce54c641b0",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9fcd2c84f6ca4ed88b1b4cf2a09422de",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44f1681ed0114877bd2d7bcdcc9e61a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f409527019b7460296ecd2c7ff144f31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train-00012-of-00014.parquet:   0%|          | 0.00/127M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6f5fe04ad0647cf80dc6710086edd0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train-00013-of-00014.parquet:   0%|          | 0.00/171M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30d40d6199d446bb9a447ec9974df732",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/40665 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "# Login using e.g. `huggingface-cli login` to access this dataset\n",
    "ds = load_dataset(\"open-r1/codeforces-cots\", \"solutions_decontaminated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "id": "pim3Grca-GIa",
    "outputId": "1e3aac57-352c-451d-d050-70bd7912d54e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nYou will be given a competitive programming problem. Please reason step by step about the solution, then provide a complete implementation in C++17.\\n\\nYour solution must read input from standard input (cin), write output to standard output (cout).\\nDo not include any debug prints or additional output.\\n\\nPut your final solution within a single code block:\\n```cpp\\n<your code here>\\n```\\n\\n# Problem\\n\\nYou are given an array $$$a$$$ of $$$n$$$ integers, where $$$n$$$ is odd.\\n\\nIn one operation, you will remove two adjacent elements from the array $$$a$$$, and then concatenate the remaining parts of the array. For example, given the array $$$[4,7,4,2,9]$$$, we can obtain the arrays $$$[4,2,9]$$$ and $$$[4,7,9]$$$ by the operations $$$[\\\\underline{4,7}, 4,2,9] \\\\to [4,2,9]$$$ and $$$[4,7,\\\\underline{4,2},9] \\\\to [4,7,9]$$$ respectively. However, we cannot obtain the array $$$[7,2,9]$$$ as it requires deleting non-adjacent elements $$$[\\\\underline{4},7,\\\\underline{4},2,9]$$$.\\n\\nYou will repeatedly perform this operation until exactly one element remains in $$$a$$$.\\n\\nFind the maximum possible value of the remaining element in $$$a$$$.\\n\\nExecution time limit: 1.0 seconds\\nMemory limit: 256.0 MB\\n\\n## Input Format\\nEach test contains multiple test cases. The first line contains a single integer $$$t$$$ ($$$1 \\\\le t \\\\le 1000$$$)\\xa0\u2014 the number of test cases. The description of test cases follows.\\n\\nThe first line of each test case contains a single integer $$$n$$$ ($$$1 \\\\le n \\\\le 99$$$; $$$n$$$ is odd)\\xa0\u2014 the length of the array $$$a$$$.\\n\\nThe second line of each test case contains $$$n$$$ integers $$$a_1, a_2, \\\\ldots, a_n$$$ ($$$1 \\\\le a_i \\\\le 100$$$)\\xa0\u2014 the elements of the array $$$a$$$.\\n\\nNote that there is no bound on the sum of $$$n$$$ over all test cases.\\n\\n## Output Format\\nFor each test case, output a single integer\\xa0\u2014 the maximum possible value of the remaining element in $$$a$$$.\\n\\n## Examples\\n```input\\n4\\n1\\n6\\n3\\n1 3 2\\n5\\n4 7 4 2 9\\n7\\n3 1 4 1 5 9 2\\n```\\n```output\\n6\\n2\\n9\\n5\\n```\\n\\n## Note\\nIn the first test case, the array $$$a$$$ is $$$[6]$$$. Since there is only one element, no operations are needed. The maximum possible value of the remaining element is $$$6$$$.\\n\\nIn the second test case, the array $$$a$$$ is $$$[1, 3, 2]$$$. We can remove the first two elements $$$[\\\\underline{1, 3}, 2] \\\\to [2]$$$, or remove the last two elements $$$[1, \\\\underline{3, 2}] \\\\to [1]$$$. Therefore, the maximum possible value of the remaining element is $$$2$$$.\\n\\nIn the third test case, the array $$$a$$$ is $$$[4, 7, 4, 2, 9]$$$. One way to maximize the remaining element is $$$[4, \\\\underline{7, 4}, 2, 9] \\\\to [\\\\underline{4, 2}, 9] \\\\to [9]$$$. Therefore, the maximum possible value of the remaining element is $$$9$$$.\\n\\nIn the fourth test case, the array $$$a$$$ is $$$[3, 1, 4, 1, 5, 9, 2]$$$. It can be shown that the maximum possible value of the remaining element is $$$5$$$.\\n<|im_end|>\\n<|im_start|>assistant\\n<think>\\nOkay, let's try to figure out how to solve this problem. The problem is that we have an array of odd length, and we can repeatedly remove pairs of adjacent elements until there's one left. We need to find the maximum possible remaining element after all possible operations.\\n\\nHmm. Let's think about the process. Each operation removes two adjacent elements. Since the array length is odd, after each operation the length decreases by 1 (because 2 are removed, so the new length is n-2 + ... wait, no. Wait, when you remove two adjacent elements, the array's length reduces by 2 each time. Wait, the original array length is n, which is odd. Then after each operation, the length becomes n-2, which is odd minus 2, which is even? Wait no, wait. Wait, n is odd. For example, if n is 3, after removing two elements, the length is 1. Which is odd. Wait, so each operation reduces the length by 2. So the parity of the length remains odd. Because subtracting 2 from an odd number gives another odd number. Wait, no. Wait 3-2=1 (odd), 1-2=-1, which is not possible. So the process stops when the length is 1. So for the starting n which is odd, after each operation the length is n-2, which is again odd, until it reaches 1.\\n\\nSo the process will take (n-1)/2 steps. Because each step removes two elements, so (n-1)/2 steps to get from n to 1.\\n\\nBut how to model which elements can be kept. The key is to find a selection of elements where each step removes two adjacent elements, and the remaining elements form a possible path of removal steps leading to that element being the last one.\\n\\nWait, perhaps there's a pattern here. Let's look at the examples.\\n\\nIn the second test case: 3 elements. The options are to remove the first two, leaving the third, or remove the last two, leaving the first. So the maximum is the maximum of the first or third element. But in the example given, the array is [1,3,2]. The options are 2 or 1, so maximum is 2. But the third element is 2. Wait, so that's the case. So for n=3, the maximum is the maximum of the elements at positions 0, 2 (assuming 0-based indexing). Because after removing the first two, you get the third. Or removing the last two, you get the first.\\n\\nWait, that's interesting. So for n=3, the last remaining element can be either the first or the third. So the maximum is the maximum of those two.\\n\\nWhat about n=5. Let's take the third example: [4,7,4,2,9]. The solution was 9, which is the fifth element. How did that happen? Let's see the steps. They first removed 7 and 4 (the second and third elements), leading to [4,2,9]. Then in that array, they removed 4 and 2, leading to 9. So the final element is the fifth element of the original array.\\n\\nAlternatively, for a 5-element array, maybe the possible elements that can remain are those at positions 0, 2, 4. Because when you start with 5 elements, after each removal step, you have 3, then 1. So maybe the positions that are even indices in the original array can be the ones that are kept.\\n\\nWait, perhaps the answer is that the maximum element among the elements at the even indices (0, 2, 4, etc.) is the answer. Because when you have an array of odd length, each time you remove two adjacent elements, the remaining elements' positions depend on which pair was removed.\\n\\nWait, for example, in the 5-element array. Let's say the array is a0, a1, a2, a3, a4. If you remove a1 and a2, then the remaining array is a0, a3, a4. Then, in the next step, you can remove a0 and a3 (leaving a4), or a3 and a4 (leaving a0). So in this path, the final element is a4 or a0.\\n\\nAlternatively, if you first remove a3 and a4, then the remaining array is a0, a1, a2. Then next step removes a0 and a1, leaving a2. So the final element is a2.\\n\\nAnother possibility: first remove a0 and a1, leaving a2, a3, a4. Then next step removes a3 and a4, leaving a2. So the final element is a2.\\n\\nSo for a 5-element array, the possible final elements are a0, a2, a4. So the maximum is the maximum of these three.\\n\\nIf that's the case, then for the third example in the input, the elements at even indices (0,2,4) are 4,4,9. The maximum is 9, which matches the sample output.\\n\\nSimilarly, in the fourth example, the input array is [3,1,4,1,5,9,2] (n=7). The even indices are 0,2,4,6. The elements are 3,4,5,2. The maximum is 5, which matches the sample output.\\n\\nAnother sample is the second test case (n=3): even indices 0 and 2. Elements are 1 and 2. Max is 2.\\n\\nSo this seems to fit all the examples.\\n\\nSo the solution is: for the array of odd length n, the maximum possible remaining element is the maximum among all elements at even indices (0-based). Because those are the positions that could end up as the last element after removing pairs in a certain way.\\n\\nWait, why is that the case? Let's think recursively. For an array of length 1, the only element is the answer. For an array of length 3, you can remove the first two elements (leaving the third) or the last two (leaving the first). So the answer is max of first and third. For length 5, the possible elements that can be left are the first, third, fifth elements, because each step of removing pairs can leave the even positions.\\n\\nBut how to see this pattern holds for any odd n?\\n\\nHmm. Let's think for n=5. The possible elements that can be left after all operations are the elements at positions 0, 2, 4.\\n\\nSimilarly, for n=7, the possible elements are 0, 2,4,6.\\n\\nSo the pattern is that the positions that can be the last element are those with even indices (0, 2, 4, ..., n-1). Because when you remove pairs, the even indices can be preserved.\\n\\nWait, but how exactly? For example, in the 5-element array, how can a2 be preserved?\\n\\nIn the example where you first remove a3 and a4, then the array is a0, a1, a2. Then you can remove a0 and a1, leaving a2. So in that case, a2 is the last element.\\n\\nSo the key insight is that when you remove pairs in such a way that certain elements are skipped, you can end up with any even index element as the last remaining.\\n\\nTherefore, the answer is simply the maximum value among the elements at even positions (0, 2, 4, ...) in the original array.\\n\\nSo the solution is to iterate over all elements in even indices and find the maximum.\\n\\nThat's a very simple solution. So the code would be, for each test case, read the array, iterate through the elements at even indices (0, 2, 4, etc.), and take the maximum.\\n\\nLet me check the samples again.\\n\\nFirst sample: n=1. The only element is at index 0, which is even. So answer is 6. Correct.\\n\\nSecond sample: n=3. Indices 0 and 2. Max of 1 and 2 is 2. Correct.\\n\\nThird sample: n=5. Indices 0, 2,4. Values 4,4,9. Max 9. Correct.\\n\\nFourth sample: n=7. Indices 0,2,4,6. Values 3,4,5,2. Max 5. Correct.\\n\\nYes, all the samples are covered by this approach.\\n\\nSo the problem reduces to selecting the maximum element from the even indices of the array.\\n\\nTherefore, the code is straightforward. Read t test cases. For each, read n, then the array. Then loop through the array, check the elements at even indices (i=0,2,4,...,n-1), and take the maximum.\\n\\nThat's it. So this solution should work.\\n\\nSo the code would be:\\n\\nFor each test case:\\n\\nRead n.\\n\\nRead the array a.\\n\\nInitialize max_val to a[0].\\n\\nThen for i from 2 to n-1, stepping by 2:\\n\\nif a[i] > max_val, update max_val.\\n\\nThen output max_val.\\n\\nYes.\\n\\nNow let's write the code.\\n\\nIn C++, the steps would be:\\n\\nRead t.\\n\\nLoop t times:\\n\\nRead n.\\n\\nRead n integers into a vector.\\n\\nInitialize the maximum as a[0].\\n\\nLoop i from 2 to n-1, step 2 (i += 2):\\n\\nCompare and update the maximum.\\n\\nPrint the maximum.\\n\\nWait, for n=1, the loop runs from 2 to 0, which is not executed. So the maximum remains a[0], which is correct.\\n\\nYes.\\n\\nSo the code would look like:\\n\\n#include <bits/stdc++.h>\\nusing namespace std;\\n\\nint main() {\\n    int t;\\n    cin >> t;\\n    while (t--) {\\n        int n;\\n        cin >> n;\\n        vector<int> a(n);\\n        for (int i=0; i<n; i++) {\\n            cin >> a[i];\\n        }\\n        int max_val = a[0];\\n        for (int i=2; i <n; i +=2) {\\n            if (a[i] > max_val) {\\n                max_val = a[i];\\n            }\\n        }\\n        cout << max_val << endl;\\n    }\\n    return 0;\\n}\\n\\nTesting this code against the samples.\\n\\nSample 1:\\n\\nInput:\\n\\n4\\n\\n1\\n\\n6\\n\\nOutput 6.\\n\\nSample 2:\\n\\n3\\n\\n1 3 2. The even indices are 0 and 2. Values 1 and 2. Max is 2.\\n\\nSample3:\\n\\n5\\n\\n4 7 4 2 9. Even indices 0,2,4. Values 4,4,9. Max is9.\\n\\nSample4:\\n\\n7\\n\\n3 1 4 1 5 9 2. Even indices 0 (3), 2 (4), 4 (5), 6 (2). Max is 5.\\n\\nAll correct.\\n\\nSo this code should pass all test cases.\\n\\nThus, the solution is to take the maximum among elements at even indices.\\n</think>\\n\\n```cpp\\n#include <bits/stdc++.h>\\nusing namespace std;\\n\\nint main() {\\n    int t;\\n    cin >> t;\\n    while (t--) {\\n        int n;\\n        cin >> n;\\n        vector<int> a(n);\\n        for (int i = 0; i < n; ++i) {\\n            cin >> a[i];\\n        }\\n        int max_val = a[0];\\n        for (int i = 2; i < n; i += 2) {\\n            if (a[i] > max_val) {\\n                max_val = a[i];\\n            }\\n        }\\n        cout << max_val << '\\\\n';\\n    }\\n    return 0;\\n}\\n```<|im_end|>\\n\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.apply_chat_template(\n",
    "    ds[\"train\"][\"messages\"][0], tokenize=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "vPLgr6sxBmOY"
   },
   "outputs": [],
   "source": [
    "\n",
    "def formatting_prompts_func(example):\n",
    "    output_texts = []\n",
    "    for i in range(len(example['problem'])):\n",
    "        text = f\"### Problem: {example['problem'][i]}\\n ### Response: {example['generation'][i]}\"\n",
    "        output_texts.append(text)\n",
    "    return output_texts\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "e92eb62e284743b6af284638a8da591b",
      "0360fb88da70455f8dee3448741da9f6",
      "507ba8e73c8c451db278848ee054e130",
      "c6f3eb06ddb945e78916ae91482ff0ce",
      "1470b5ec997b42aba820870ae023e169",
      "85dc6838069f40b4a8db958765eb413c",
      "c43a088652a34343bdc6899cb7ee48f1",
      "51794cec056440b4ab0fd9215c2004b5",
      "271e98a654ba4c02b06083d00bf664d1",
      "a1525ae5572549e7bb6272d912ba554d",
      "590c10a5c3da4db6bd1dfb767632fe82"
     ]
    },
    "id": "95_Nn-89DhsL",
    "outputId": "5e67e3b1-efef-44dd-b027-895c02903b1c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e92eb62e284743b6af284638a8da591b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/40665 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from trl import SFTTrainer, SFTConfig\n",
    "from transformers import TrainingArguments\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = ds[\"train\"],\n",
    "    max_seq_length = max_seq_length,\n",
    "    formatting_func = formatting_prompts_func,\n",
    "    packing = False, # Can make training 5x faster for short sequences.\n",
    "    args = SFTConfig(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        warmup_steps = 5,\n",
    "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
    "        max_steps = 60,\n",
    "        learning_rate = 2e-4,\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.001,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "        report_to = \"none\", # Use TrackIO/WandB etc\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "83919595-69a9-42c4-8ef3-130533a68257"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.557 GB.\n",
      "5.393 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "# @title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "2e4fa5ba-c9ba-4b59-d5c4-ba723c799992"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
      "   \\\\   /|    Num examples = 40,665 | Num Epochs = 1 | Total steps = 60\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient accumulation steps = 4\n",
      "\\        /    Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n",
      " \"-____-\"     Trainable parameters = 40,370,176/7,000,000,000 (0.58% trained)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unsloth: Will smartly offload gradients to save VRAM!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [60/60 43:58, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.012300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.970100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.012100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.983100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.033700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.993100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.060200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.953000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.936100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.949000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.925800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.909400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.949100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.887300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.910500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.907400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.898400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.855000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.872300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.862700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.824000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.868400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.877200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.850800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.838100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.866100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.839700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.847200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.829900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.846100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.857900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.808500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.802400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.814300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.826800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.792100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.806300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.845900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.838900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.782800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.641500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.769200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.786600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.791500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.801300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.750400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.767300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.822300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.852300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.788200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.799200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.803700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.781700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.831200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.766100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.710100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pCqnaKmlO1U9",
    "outputId": "3005b362-2b86-468a-8f28-8effd9208566"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "462.3942 seconds used for training.\n",
      "7.71 minutes used for training.\n",
      "Peak reserved memory = 7.893 GB.\n",
      "Peak reserved memory for training = 2.129 GB.\n",
      "Peak reserved memory % of max memory = 53.519 %.\n",
      "Peak reserved memory for training % of max memory = 14.436 %.\n"
     ]
    }
   ],
   "source": [
    "# @title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory / max_memory * 100, 3)\n",
    "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(\n",
    "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
    ")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model! You can change the instruction and input - leave the output blank!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "ef815e2b-2095-4c27-d2af-a6d3007a8f68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A tech company, \"DataGoSlow,\" produces high-performance memory modules. They sell these modules in \\'n\\' different retail outlets. Each outlet sells the modules at a unique price, represented by \\'xi\\' dollars.\\n\\nA large data center plans to purchase these memory modules over \\'q\\' consecutive procurement cycles. For each cycle, the data center has a budget constraint \\'mi\\' dollars. They want to know how many outlets sell the memory modules at a price *strictly less than* their budget.\\n\\n**Constraints:**\\n\\n* Time limit per test: 2.0 seconds\\n* Memory limit per test: 256.0 megabytes\\n\\n**Input Format:**\\n\\n* The first line of the input contains a single integer \\'n\\' (1 \u2264 n \u2264 100 000) \u2014 the number of retail outlets.\\n* The second line contains \\'n\\' integers \\'xi\\' (1 \u2264 xi \u2264 100 000) \u2014 prices of the memory modules in each outlet.\\n* The third line contains a single integer \\'q\\' (1 \u2264 q \u2264 100 000) \u2014 the number of procurement cycles.\\n* Then follow \\'q\\' lines, each containing one integer \\'mi\\' (1 \u2264 mi \u2264 109) \u2014 the budget for the i-th procurement cycle.\\n\\n**Output Format:**\\n\\n* Print \\'q\\' integers. The i-th of them should be equal to the number of outlets where the memory modules can be purchased at a price *strictly less than* the budget for the i-th cycle.\\n\\n**Example:**\\n\\n```input\\n5\\n3 10 8 6 11\\n4\\n1\\n10\\n3\\n11\\n```\\n\\n```output\\n0\\n2\\n3\\n0\\n```\\n\\n**Note:**\\n\\nIn the first and fourth cases, there are no such outlets.\\n\\nIn the second case, the first and third outlets have prices less than 10.\\n\\nIn the third case, all three outlets have prices less than ']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_problem = \"\"\"A tech company, \"DataGoSlow,\" produces high-performance memory modules. They sell these modules in 'n' different retail outlets. Each outlet sells the modules at a unique price, represented by 'xi' dollars.\n",
    "\n",
    "A large data center plans to purchase these memory modules over 'q' consecutive procurement cycles. For each cycle, the data center has a budget constraint 'mi' dollars. They want to know how many outlets sell the memory modules at a price *strictly less than* their budget.\n",
    "\n",
    "**Constraints:**\n",
    "\n",
    "* Time limit per test: 2.0 seconds\n",
    "* Memory limit per test: 256.0 megabytes\n",
    "\n",
    "**Input Format:**\n",
    "\n",
    "* The first line of the input contains a single integer 'n' (1 \u2264 n \u2264 100 000) \u2014 the number of retail outlets.\n",
    "* The second line contains 'n' integers 'xi' (1 \u2264 xi \u2264 100 000) \u2014 prices of the memory modules in each outlet.\n",
    "* The third line contains a single integer 'q' (1 \u2264 q \u2264 100 000) \u2014 the number of procurement cycles.\n",
    "* Then follow 'q' lines, each containing one integer 'mi' (1 \u2264 mi \u2264 109) \u2014 the budget for the i-th procurement cycle.\n",
    "\n",
    "**Output Format:**\n",
    "\n",
    "* Print 'q' integers. The i-th of them should be equal to the number of outlets where the memory modules can be purchased at a price *strictly less than* the budget for the i-th cycle.\n",
    "\n",
    "**Example:**\n",
    "\n",
    "```input\n",
    "5\n",
    "3 10 8 6 11\n",
    "4\n",
    "1\n",
    "10\n",
    "3\n",
    "11\"\"\"\n",
    "\n",
    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "inputs = tokenizer([example_problem], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
    "tokenizer.batch_decode(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrSvZObor0lY"
   },
   "source": [
    " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e2pEuRb1r2Vg",
    "outputId": "187a7d22-bde7-439a-b1b0-a3e0dc308843"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
      "\n",
      "### Instruction:\n",
      "Continue the fibonnaci sequence.\n",
      "\n",
      "### Input:\n",
      "1, 1, 2, 3, 5, 8\n",
      "\n",
      "### Response:\n",
      "13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, \n"
     ]
    }
   ],
   "source": [
    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "upcOlWe7A1vc",
    "outputId": "0995f3eb-3eb9-423f-ede2-3b7f2f9f10c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('lora_model/tokenizer_config.json',\n",
       " 'lora_model/special_tokens_map.json',\n",
       " 'lora_model/vocab.json',\n",
       " 'lora_model/merges.txt',\n",
       " 'lora_model/added_tokens.json',\n",
       " 'lora_model/tokenizer.json')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.save_pretrained(\"lora_model\")  # Local saving\n",
    "tokenizer.save_pretrained(\"lora_model\")\n",
    "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
    "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MKX_XKs_BNZR",
    "outputId": "0412171f-a9b4-4018-a4aa-d9725d2b8201"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
      "\n",
      "### Instruction:\n",
      "What is a famous tall tower in Paris?\n",
      "\n",
      "### Input:\n",
      "\n",
      "\n",
      "### Response:\n",
      "One of the most famous tall towers in Paris is the Eiffel Tower. It was built by Gustave Eiffel in 1889 and stands at a height of 324 meters (1,063 feet). It is a symbol of Paris and one of the most recognizable structures in the world.<|endoftext|>\n"
     ]
    }
   ],
   "source": [
    "if False:\n",
    "    from unsloth import FastLanguageModel\n",
    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        max_seq_length = max_seq_length,\n",
    "        dtype = dtype,\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "\n",
    "# alpaca_prompt = You MUST copy from above!\n",
    "\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        \"What is a famous tall tower in Paris?\", # instruction\n",
    "        \"\", # input\n",
    "        \"\", # output - leave this blank for generation!\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQMjaNrjsU5_"
   },
   "source": [
    "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yFfaXG0WsQuE"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    # I highly do NOT suggest - use Unsloth if possible\n",
    "    from peft import AutoPeftModelForCausalLM\n",
    "    from transformers import AutoTokenizer\n",
    "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "# Merge to 16bit\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
    "\n",
    "# Merge to 4bit\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
    "\n",
    "# Just LoRA adapters\n",
    "if False:\n",
    "    model.save_pretrained(\"model\")\n",
    "    tokenizer.save_pretrained(\"model\")\n",
    "if False:\n",
    "    model.push_to_hub(\"hf/model\", token = \"\")\n",
    "    tokenizer.push_to_hub(\"hf/model\", token = \"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TCv4vXHd61i7"
   },
   "source": [
    "### GGUF / llama.cpp Conversion\n",
    "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
    "\n",
    "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
    "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
    "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
    "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
    "\n",
    "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FqfebeAdT073"
   },
   "outputs": [],
   "source": [
    "# Save to 8bit Q8_0\n",
    "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
    "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
    "# And change hf to your username!\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
    "\n",
    "# Save to 16bit GGUF\n",
    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
    "\n",
    "# Save to q4_k_m GGUF\n",
    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
    "\n",
    "# Save to multiple GGUF options - much faster if you want multiple!\n",
    "if False:\n",
    "    model.push_to_hub_gguf(\n",
    "        \"hf/model\", # Change hf to your username!\n",
    "        tokenizer,\n",
    "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
    "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FyKl34TVyjv-"
   },
   "source": [
    "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp.\n",
    "\n",
    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
    "\n",
    "Some other links:\n",
    "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
    "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
    "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
    "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
    "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
    "\n",
    "  Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
    "</div>\n",
    "\n  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n"
   ]
  }
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